mtasemotif 0.1.1

Local-first MTase candidate + methylation motif prediction pipeline

mtase-motif

mtase-motif is a local-first Python package for finding bacterial DNA methyltransferase candidates and transferring or inferring methylation motifs from a single genome.

The package keeps the database management code in mtase_motif/, but it does not bundle downloaded Pfam, TIGRFAMs, or REBASE payloads in the published artifact.

Install

Install the Python package in a local virtual environment:

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e '.[dev]'

For a PyPI release, install the distribution as:

python -m pip install mtasemotif

The installed CLI command remains:

mtase-motif --help

Install native executables separately. The repo includes an optional environment.yml for a conda env that provides the non-Python tools:

conda env create -f environment.yml
conda activate mtase

End-To-End Walkthrough

The commands below cover the full sequence-first workflow from a clean checkout to a completed run on the example E. coli genome stored on this machine at /Users/li/data/hammerhead_test/ecoli.fa. That path is machine-local, not part of the checkout.

1. Prepare the environments

Activate the native-tool conda environment first, then the local Python venv:

conda activate mtase
source .venv/bin/activate

This keeps prodigal, hmmscan, hmmsearch, hmmpress, mmseqs or blastp, and fimo on PATH while the Python package stays in .venv. The conda environment provides the HMMER tools used for TIGRFAMs, but it does not download the TIGRFAMs model library itself.

2. Create and populate the local database

The walkthrough below uses a project-local DB directory so the full run is self-contained:

DB_DIR=$PWD/db

mtase-motif db init --db-dir "$DB_DIR"
mtase-motif db fetch pfam --db-dir "$DB_DIR"
mtase-motif db fetch rebase --db-dir "$DB_DIR"
mtase-motif db index --db-dir "$DB_DIR"
mtase-motif db status --db-dir "$DB_DIR"

Downloaded databases live outside the package by default under ~/.cache/mtase-motif/db, but using --db-dir "$DB_DIR" keeps this walkthrough local to the repo.

The curated Pfam subset includes MTase catalytic domains plus Type I context models (PF02384, PF12161, PF01420, and PF04313). Pfam-only runs can therefore recover Type I-like candidates when the MTase core/support domains or nearby S/R context are present. TIGRFAMs remains optional, but it adds direct HsdM/S/R and Type III Mod/Res context and can make type hints more specific.

To enable TIGRFAMs, download or otherwise obtain the TIGRFAMs HMM library separately, then point --source at the file or at a directory containing one of these names:

• TIGRFAMs.hmm
• TIGRFAMs.hmm.gz
• TIGRFAMs_15.0_HMM.LIB
• TIGRFAMs_15.0_HMM.LIB.gz
• another *.HMM.LIB or *.HMM.LIB.gz

Then import and rebuild indexes:

mtase-motif db fetch tigrfams --db-dir "$DB_DIR" --source /path/to/TIGRFAMs_15.0_HMM.LIB.gz
mtase-motif db index --db-dir "$DB_DIR"
mtase-motif db status --db-dir "$DB_DIR"

db index needs hmmpress; mtase-motif run uses hmmsearch when the TIGRFAMs subset is present.

Offline and local-mirror examples:

mtase-motif db fetch pfam --db-dir "$DB_DIR" --source /path/to/Pfam-A.hmm.gz
mtase-motif db fetch rebase --db-dir "$DB_DIR" --source /path/to/rebase_emboss_dir
mtase-motif db index --db-dir "$DB_DIR"

For offline REBASE, --source alone is not enough. The source directory must also contain rebase_proteins.faa or one or more REBASE *_Protein.txt protein dumps before mtase-motif db index can build the REBASE protein search index.

More database download and import notes are in docs/database_setup.md.

3. Run motif prediction on a genome FASTA

Use the example genome from /Users/li/data/hammerhead_test:

GENOME=/Users/li/data/hammerhead_test/ecoli.fa
OUT_DIR=$PWD/results/ecoli

mtase-motif run --genome "$GENOME" --db-dir "$DB_DIR" --out "$OUT_DIR" -j 4

Replace "$GENOME" with your own .fa or .fna file when running on a new genome.

Optional structure-assisted runs require local candidate structures via --structures-dir. Foldseek-backed steps also require foldseek on PATH and either --foldseek-db or the default DB at <db-dir>/structures/pdb/foldseek_db; --foldseek-labels only applies when that Foldseek DB-backed search path is available.

If you already have predicted proteins, you can skip gene calling:

mtase-motif run --genome "$GENOME" --proteins /path/to/proteins.faa --db-dir "$DB_DIR" --out "$OUT_DIR" -j 4

4. Inspect the outputs

Core outputs in "$OUT_DIR":

• "$OUT_DIR"/mtase_candidates.tsv
• "$OUT_DIR"/motif_calls.tsv
• "$OUT_DIR"/motif_assignment.tsv
• "$OUT_DIR"/summary.tsv
• "$OUT_DIR"/<candidate_id>/motif/pwm.meme
• "$OUT_DIR"/<candidate_id>/fimo/fimo.tsv
• "$OUT_DIR"/<candidate_id>/qc/qc.json

motif_calls.tsv now carries derived motif semantics such as mod_position, motif_class, canonical/reverse-complement forms, and an overall assignment_state. motif_assignment.tsv separates the primary call from alternate related-candidate or hint routes when a candidate is unresolved or ambiguous.

Quick checks:

ls "$OUT_DIR"
head "$OUT_DIR/mtase_candidates.tsv"
head "$OUT_DIR/summary.tsv"

Runtime Tools

These executables must be on PATH for the sequence-first workflow:

• prodigal
• hmmscan, hmmsearch, hmmpress
• mmseqs or blastp plus makeblastdb
• fimo

Development

make lint
make test
make sdist-check
make package-check

Release notes live in CHANGELOG.md. Before tagging a release, bump mtase_motif/__init__.py, run make package-check, then push a matching tag such as v0.1.1. Tagged releases are set up for GitHub Releases and PyPI publishing through the GitHub Actions workflows in .github/workflows/.